Natural language processing (NLP) and machine learning are arguably the trendiest components of artificial intelligence (AI). The terms are discussed at length in think pieces, scientific journals and even have their own hashtags on Twitter. Discussed far less often, yet incredibly important for any company striving to stay ahead of the curve, is predictive analytics.
We’re well into the age of analytics, and being a “data-driven company” is cost of entry for most industries. While traditional analytics is focused on pulling insights from past data, predictive analytics is all about forecasting the future—with a greater degree of accuracy.
In a nutshell, predictive analytics uses data mining and statistical modeling techniques to identify the likelihood of future outcomes based on historical data. It’s the difference between how many people visited your site last month, and how many are likely to visit if you do x, or y, or z.
This is important because…
Speed is everything! Technology shifts, consumer behaviors change, If you’re developing a product, you need to know what your market will look like when it launches, not when you began development. You need to know what a reader will click/like/buy/share tomorrow more than you need to know what they did last week. Bridging that knowledge gap is something that smart people have been doing pretty effectively for a long time, but as companies collect more and more data, it becomes exponentially harder to keep up with people power alone.
Where does predictive analytics fit into AI?
Predictive analytics is an essential part of machine learning and the automation of advanced tasks in marketing, sales and operations. It’s also just really powerful analytics. It takes the insights of traditional analytics and goes a step further, producing a series of recommended actions and calculating the likelihood that each will result in a desired (or undesired) outcome.
This is just a snippet of the business applications and industries in which predictive analytics can make a significant difference.
- CRM: Analyzing customer behaviors to predict what actions at what time will boost acquisition, retention, engagement, win-back.
- Medical: Determine patient risk for certain condition based on behaviors and demographics
- Insurance: predictive analytics is transforming the way underwriters predict and manage risk
- Content Marketing: Predict what content at what time is likely to get individual customers to click/buy/share.
- Sports: Remember Sabermetrics? Add AI to the mix, and you can isolate individual factors that can boost individual and team performance and adapt strategies in real time to specific opponents and scenarios.